The parameter values for training the proposed network are empirically tuned to achieve the best performance for both the face and expression recognition tasks, as presented in Table REF . This table presents the optimal parameter values for each sub-network as well as for the whole deep network, CapsField. As can be observed, the network settings for both face and expression recognition tasks are rather similar except for: i) the choice of CNNs, as the two tasks use different CNN models for spatial feature extraction; and ii) the training batch size, as the number of input samples is different for the face and expression recognition tasks. Concerning the number of routing iterations, different values ranging from 2 to 6 have been considered, where increasing the value to more than 3 did not improve the performance while increasing the computational time. The entire architecture has been implemented using TensorFlow [1]} with Keras backend and is trained using a GeForce GTX 1080 GPU.
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